1. Field of the Disclosed Embodiments
The disclosure relates to automated assignment of geodetic coordinates to images of aerial video.
2. Introduction
Manual methods are still required to geoposition aerial video as the lack of accurate camera models prevents automation. To overlay video on background imagery, a human analyst must extract a video frame and align it with imagery. To do this with accuracy over a mountainous area requires training in photogrammetry and a Digital Point Positioning Database (DPPDB), and it is an awkward process. The government has tried to automate the geopositioning of video in programs such as the National Geospatial-Intelligence Agency's (NGA's) GRIDLOCK, but these attempts have all failed. Complications include terrain uncertainties as well as the lack of accurate global positioning system (GPS) and inertial measurement unit (IMU) information. The best systems often have timing errors that create large uncertainties in the camera positions, and even when the systems are operating optimally, there are still errors of 50 meters or more. With several aerial systems, positioning errors of several hundred meters are not uncommon.
Programs are currently building an aerial video event detection system to aid military analysts. Some of the programs that have events that require geopositioning and cannot be handled. For example, the event, “Person entering building” could not be handled due to lack of geopositioning information. Simple tripwire events require a human analyst to mark the tripwire, but such manual intervention defeats the purpose of automation and should not be required. Furthermore, the lack of accurate geopositioning leads to misalignments that defeat tripwire event detection.
Automated video camera model determination processes have been used but this research is still immature. These processes place constraints on the trajectory of the video platform. Typically the platform must fly completely around the scene being imaged. If the platform hovers or does not change its position significantly, the process will not work. In general, sometimes the process works on aerial video, but more often than not, it fails. Even when it works, the process cannot geoposition the video. It only positions it relative to a synthetic coordinate frame. The user must manually add more information and transform this synthetic frame into a geographic coordinate frame.
Video stabilization can sometimes be accomplished by automated processes over very small areas where the scene is relatively flat. Over a large area where there is significant terrain relief, these processes do not work. In high-resolution wide area motion imagery systems (WAMI) such as a Government program entitled, “Constant Hawk”, the problem of stabilization is still an area of research. However, it is unclear at this time how much success researchers will have with stabilizing the video over 3D structures like buildings and mountains.